Analyzing the structure of a musical piece is a well-known task in any music theory
or musicological field. However, in recent years, trying to find a way of performing
such task in an automated manner has experienced a considerable increase in
interest within the music information retrieval (MIR) field. Nonetheless, up to this
day, the task of automatically segmenting and analyzing such structures remains an
open challenge, with results that are still far from human performance. This thesis
presents a novel approach to the task of automatic segmentation and annotation
of musical structure by introducing a supervised approach that can take advantage
of the information about the music structure of previously annotated pieces. The
approach is tested over three different datasets with varying degrees of success.
We show how a supervised approach has the potential to outperform state-of-the-art
algorithms assuming a large and varied enough dataset is used. The approach
is evaluated by computing standard evaluation metrics in order to compare the
obtained results with other approaches. Several case studies that are considered
relevant are as well presented, along with future implications.